Maaike S.M. van Mourik1,
1Dept. of Medical Microbiology and Infection Control. University Medical Centre Utrecht, Utrecht, the Netherlands.
Traditional surveillance of healthcare-associated infections (HAI) through manual chart review is time consuming and prone to error and differences in interpretation. Increasing adoption of electronic health records has fuelled the development of (semi-)automated surveillance systems for the most commonly encountered types of HAI.
A transition towards automated surveillance requires a re-design of surveillance programs and an explicit choice regarding the intended aims of surveillance and selection of the most-appropriate surveillance approach. Semi-automated surveillance can achieve a reduced workload while maintaining some room for flexibility whereas fully automated systems may achieve a higher degree of standardization, possibly at the cost of clinical relevance. In line with these choices, definitions for automated surveillance require adaptation in order to make automation feasible, but absence of clinical information can hamper clinical utility. Examples of existing automated surveillance systems include semi-automated surveillance of surgical site infections and fully automated surveillance of bacteraemia. The design of automated surveillance systems will also depend on the possibilities within the healthcare IT landscape and requires careful validation of the methods deployed.
Several strategies are possible when aiming to develop automated surveillance within surveillance networks. The PRAISE network defined two main strategies towards large-scale automated surveillance, so-called locally-implemented and centrally-implemented automated surveillance, each with its own advantages and pitfalls.